Neuro-response data synchronization

ABSTRACT

An example system includes a headset to gather first data comprising first neuro-response data and second neuro-response data from a user while the user is exposed to stimulus material. In the example system, the headset comprises a first sensor to gather the first neuro-response data, the first neuro-response data comprising at least one of electroencephalographic data or magnetoencephalographic data, and a second sensor to gather the second neuro-response data, the second neuro-response data comprising facial emotion encoding data. The headset also comprises a processor to synchronize the first neuro-response data, the second neuro-response data and the stimulus material to generate synchronized data and determine an effectiveness of a portion of the stimulus material based on the synchronized data.

RELATED APPLICATIONS

This patent arises from a continuation of U.S. patent application Ser.No. 12/778,828, now U.S. Pat. No. 8,655,428, which is entitled“Neuro-Response Data Synchronization,” filed on May 12, 2010, and ishereby incorporated by reference in its entirety.

This patent is related to U.S. patent application Ser. No. 12/056,190;U.S. patent application Ser. No. 12/056,211; U.S. patent applicationSer. No. 12/056,221; U.S. patent application Ser. No. 12/056,225; U.S.patent application Ser. No. 12/113,863; U.S. patent application Ser. No.12/113,870; U.S. patent application Ser. No. 12/122,240; U.S. patentapplication Ser. No. 12/122,253; U.S. patent application Ser. No.12/122,262; U.S. patent application Ser. No. 12/135,066; U.S. patentapplication Ser. No. 12/135,074; U.S. patent application Ser. No.12/182,851; U.S. patent application Ser. No. 12/182,874; U.S. patentapplication Ser. No. 12/199,557; U.S. patent application Ser. No.12/199,583; U.S. patent application Ser. No. 12/199,596; U.S. patentapplication Ser. No. 12/200,813; U.S. patent application Ser. No.12/234,372; U.S. patent application Ser. No. 12/135,069; U.S. patentapplication Ser. No. 12/234,388; U.S. patent application Ser. No.12/544,921; U.S. patent application Ser. No. 12/544,934; U.S. patentapplication Ser. No. 12/546,586; U.S. patent application Ser. No.12/544,958; U.S. patent application Ser. No. 12/846,242; U.S. patentapplication Ser. No. 12/410,380; U.S. patent application Ser. No.12/410,372; U.S. patent application Ser. No. 12/413,297; U.S. patentapplication Ser. No. 12/545,455; U.S. patent application Ser. No.12/608,660; U.S. patent application Ser. No. 12/608,685; U.S. patentapplication Ser. No. 13/444,149; U.S. patent application Ser. No.12/608,696; U.S. patent application Ser. No. 12/731,868; U.S. patentapplication Ser. No. 13/045,457; U.S. patent application Ser. No.12/778,810; U.S. patent application Ser. No. 13/104,821; U.S. patentapplication Ser. No. 13/104,840; U.S. patent application Ser. No.12/853,197; U.S. patent application Ser. No. 12/884,034; U.S. patentapplication Ser. No. 12/868,531; U.S. patent application Ser. No.12/913,102; U.S. patent application Ser. No. 12/853,213; and U.S. patentapplication Ser. No. 13/105,774.

TECHNICAL FIELD

The present disclosure relates to portable electroencephalography (EEG)headsets and stimulus synchronization.

BACKGROUND

Conventional electroencephalography (EEG) systems use scalp levelelectrodes typically attached to elastic caps or bands to monitorneurological activity. Conductive gels and pastes are applied beforeplacement of the scalp electrodes to improve sensitivity. However,application of conductive gels and pastes is often inconvenient and timeconsuming. Furthermore, conductive gels and pastes can often bleedbetween neighboring electrodes and cause signal contamination. Elasticcaps or bands can also be uncomfortable for prolonged use. Conventionalmechanisms are often used in highly controlled laboratory environmentsunder supervision of trained technicians.

Some efforts have been made in the development of more portable,efficient, and effective EEG data collection mechanisms. However,available mechanisms have a variety of limitations. Consequently, it isdesirable to provide improved mechanisms for collecting EEG data.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure may best be understood by reference to the followingdescription taken in conjunction with the accompanying drawings, whichillustrate particular examples.

FIG. 1 illustrates one example of a system for performing neuro-responsedata synchronization.

FIGS. 2A-2E illustrate a particular example of a neuro-response datacollection mechanism. In the examples shown, the example neuro-responsedata collection mechanism includes electrodes connected to hubs on thesides of the data collection mechanism, which are rotatable, forexample, between the position shown in FIG. 2C and the position shown inFIG. 2E.

FIG. 3 illustrates examples of data models that can be used with astimulus and response repository.

FIG. 4 illustrates one example of a query that can be used with theneuro-response collection system.

FIG. 5 illustrates one example of a report generated using theneuro-response collection system.

FIG. 6 illustrates one example of a technique for performingneuro-response data synchronization.

FIG. 7 provides one example of a system that can be used to implementone or more example mechanisms or processes disclosed herein.

DETAILED DESCRIPTION

Reference will now be made in detail to some specific examplesconstructed in accordance with the teachings of the invention includingthe best modes contemplated by the inventors for carrying out theexamples disclosed herein. Specific examples are illustrated in theaccompanying drawings. It is not intended to limit the teachings of thisdisclosure to the described examples. On the contrary, it is intended tocover alternatives, modifications, and equivalents as may be includedwithin the spirit and scope of the teachings of this disclosure asdefined by the claims.

Example techniques and mechanisms disclosed herein will be described inthe context of particular types of electrodes. However, it should benoted that example techniques and mechanisms of the present disclosureapply to a variety of different types of electrodes and contacts. In thefollowing description, numerous specific details are set forth in orderto provide a thorough understanding of the present examples. Someexamples may be implemented without some or all of these specificdetails. In other instances, well known process operations have not beendescribed in detail in order not to unnecessarily obscure the teachingsof this disclosure.

Some example techniques and mechanisms will sometimes be described insingular form for clarity. However, it should be noted that someexamples include multiple iterations of a technique or multipleinstantiations of a mechanism unless noted otherwise. For example, asystem uses a processor in a variety of contexts. However, it will beappreciated that a system can use multiple processors while remainingwithin the scope of the present disclosure unless otherwise noted.Furthermore, the example techniques and mechanisms of the presentdisclosure will sometimes describe a connection between two entities. Itshould be noted that a connection between two entities does notnecessarily mean a direct, unimpeded connection, as a variety of otherentities may reside between the two entities. For example, a processormay be connected to memory, but it will be appreciated that a variety ofbridges and controllers may reside between the processor and memory.Consequently, a connection does not necessarily mean a direct, unimpededconnection unless otherwise noted.

Overview

Efficient and effective mechanisms for collecting electroencephalography(EEG) data are provided to synchronize neuro-response data collectionwith stimulus material presentation for in situ engagement monitoringand tracking. An EEG headset includes multiple point electrodesindividually isolated and amplified. In some examples, a stimulusmaterial presentation mechanism includes a clock source and a clocktransmitter. The clock transmitter sends clock signals to aneuro-response data collection mechanism to allow synchronization ofneuro-response data collected with stimulus presentation events. The EEGheadset can be configured to perform processing while supporting bothcontinuous input and output.

EXAMPLES

Conventional distributed response monitoring mechanisms merely trackstimulus being viewed and rely on behavior and survey based datacollected from subjects exposed to stimulus materials. In someinstances, attempts are made to measure responses to programs andcommercials using demographic, statistical, user behavioral, and surveybased information. For example, subjects are required to completesurveys after exposure to programs and/or commercials. However, surveyresults often provide only limited information about program andcommercial response. For example, survey subjects may be unable orunwilling to express their true thoughts and feelings about a topic, orquestions may be phrased with built in bias. Articulate subjects may begiven more weight than non-expressive ones. Analysis of multiple surveyresponses and correlation of the responses to stimulus material is alsolimited. A variety of semantic, syntactic, metaphorical, cultural,social and interpretive biases and errors prevent accurate andrepeatable evaluation. Mechanisms for storing, managing, and retrievingconventional responses are also limited.

Consequently, example techniques and mechanisms of the presentdisclosure use EEG measurements to allow more accurate measurement andmonitoring of attention and engagement. According to some examples, anEEG headset is provided to subjects for use home, recreational, work, aswell as laboratory environments. In some examples, the EEG headsetincludes multiple dry electrodes individually isolated and amplified.Data from individual electrodes may be processed prior to continuoustransmission to a data analyzer. Processing may include filtering toremove noise and artifacts as well as compression and/or encryption.Individual electrodes are configured to contact the scalp in a varietyof areas while avoiding the contact with the temporal region.

According to some examples, an electric cap or band is not requiredbecause individual opposing electrodes are attached to exert somewhatopposing forces to secure a headset. In some examples, a headset springmechanism exerts elastic forces to push both frontal and rear electrodesinto close contact with the scalp. According to some examples, frontalelectrodes exert point forces that counterbalance point forces exertedby rear electrodes. Electrodes are shaped as points to reach the scalpthrough non-conductive hair follicles. One of more elastic mechanismscan be used to allow for effective counterbalancing forces. In someexamples, right side scalp electrodes counterbalance forces from leftside scalp electrodes to secure a headset, allowing front electrodes andrear electrodes to contact the scalp. It should be noted that forcesneed not perfectly counterbalance.

EEG dry electrodes allow in situ monitor and tracking of neuro-responseactivity including engagement levels. According to some examples, thedata collection mechanism is synchronized with stimulus material toallow determination of aspects of stimulus materials that evokeparticular neurological responses. In some examples, the EEG headset issynchronized with stimulus data using a shared clock or an externalclock from a cell tower or a satellite. Although a headset may merelyhave an internal clock that generates timestamps, it is recognized thattimestamps in themselves are insufficient to provide for the precisemeasurements used to determine subject neurological responses.

According to some examples, a stimulus material presentation mechanismuses a clock source to transmit clock signals to an EEG headset. Theclock source may be an external clock, timing information embedded in astimulus material presentation stream, a device clock, etc. In someexamples, the EEG headset stores neuro-response data collected from auser exposed to stimulus material for transmission to a data analyzer.Neuro-response data is synchronized with timing information associatedwith the stimulus material presentation to allow identification ofresponses and associated events in the neuro-response data. In someexamples, neuro-response data is stored with synchronized timing data toallow placement of stimulus material and neuro-response data on the sametime scale.

According to some examples, the EEG headset uses flexible printedcircuit boards (PCBs) to enhance shielding, routability andconnectability of elements including amplifiers, sensors, transmitters,etc.

A subject may wear the portable neuro-response data collection mechanismduring a variety of activities in non-laboratory settings. This allowscollection of data from a variety of sources while a subject is in anatural state. In some examples, data collection can occur effectivelyin corporate and laboratory settings, but it is recognized thatneuro-response data may even be more accurate if collected while asubject is in a more natural environment.

A variety of neurological, neuro-physiological, and effector mechanismsmay be integrated in a neuro-response data collection mechanism. EEGmeasures electrical activity associated with post synaptic currentsoccurring in the milliseconds range. Subcranial EEG can measureelectrical activity with the most accuracy, as the bone and dermallayers weaken transmission of a wide range of frequencies. Nonetheless,surface EEG provides a wealth of electrophysiological information ifanalyzed properly. Portable EEG with dry electrodes provide a largeamount of neuro-response information. It should be recognized that othermechanisms such as Electrooculography (EOG), eye tracking, facialemotion encoding, reaction time, Functional Magnetic Resonance Imaging(fMRI) and Magnetoencephalography (MEG) can also be used in someexamples.

According to some examples, the techniques and mechanisms of the presentdisclosure intelligently blend multiple modes and manifestations ofprecognitive neural signatures with cognitive neural signatures and postcognitive neurophysiological manifestations to more accurately allowmonitoring.

According to some examples, subjects may be exposed to predetermined orpreselected stimulus material. In other examples, no predetermined orpreselected stimulus material is provided and a system collectsneuro-response data for stimulus material a user is exposed to duringtypical activities.

For example, multiple subjects may be provided with portable EEGmonitoring systems with dry electrodes that allow monitoring ofneuro-response activity while subjects view billboards. Response data isanalyzed and integrated. In some examples, all response data is providedfor data analysis. In other examples, interesting response data alongwith recorded stimulus material is provided to a data analyzer.According to some examples, response data is analyzed and enhanced foreach subject and further analyzed and enhanced by integrating dataacross multiple subjects.

According to some examples, individual and integrated response data isnumerically maintained or graphically represented. Measurements formultiple subjects are analyzed to determine possible patterns,fluctuations, profiles, etc.

According to some examples, neuro-response data may show particulareffectiveness of stimulus material for a particular subset ofindividuals. A variety of stimulus materials such as entertainment andmarketing materials, media streams, billboards, print advertisements,text streams, music, performances, sensory experiences, etc. can beanalyzed. According to some examples, enhanced neuro-response data isgenerated using a data analyzer that performs both intra-modalitymeasurement enhancements and cross-modality measurement enhancements.According to some examples, brain activity is measured not just todetermine the regions of activity, but to determine interactions andtypes of interactions between various regions. The example techniquesand mechanisms of the present disclosure recognize that interactionsbetween neural regions support orchestrated and organized behavior.Attention, emotion, memory, retention, priming, and othercharacteristics are not merely based on one part of the brain butinstead rely on network interactions between brain regions.

Example techniques and mechanisms of the present disclosure furtherrecognize that different frequency bands used for multi-regionalcommunication can be indicative of the effectiveness of stimuli. In someexamples, evaluations are calibrated to each subject and synchronizedacross subjects. In some examples, templates are created for subjects tocreate a baseline for measuring pre and post stimulus differentials.According to some examples, stimulus generators are intelligent andadaptively modify specific parameters such as exposure length andduration for each subject being analyzed.

FIG. 1 illustrates one example of a system for collection ofneuro-response data. Subjects 131, 133, 135, and 137 are associated withneuro-response data collection mechanisms 141, 143, 145, and 147.According to some examples, subjects voluntarily use neuro-response datacollection mechanisms such as EEG caps, EOG sensors, recorders, cameras,etc., during exposure to particular stimulus materials provided bystimulus presentation mechanism 101 or during normal activities innon-laboratory environments. According to some examples, neuro-responsedata is measured for subjects in non-laboratory settings includinghomes, shops, workplaces, parks, theatres, etc. In some examples,neuro-response data collection mechanisms 145 and 147 include persistentstorage mechanisms and network 161 interfaces that are used to transmitcollected data to a data analyzer 181. In other examples, neuro-responsedata collection mechanisms 141 and 143 include interfaces to computersystems 151 and 153 that are configured to transmit data to a dataanalyzer 181 over one or more networks. According to some examples,stimulus material is clock synchronized with the data collectionmechanisms 141, 143, 145, and 147. In some examples, stimulus materialpresentation mechanism 101 and the data collection mechanisms 141, 143,145, and 147 are clock synchronized using a clock source 103 and a clocksignal transmitter 105. The clock source 103 may be timing informationembedded in stimulus material, a cell tower or satellite clock signal, astimulus presentation device clock, a EEG headset clock, etc. A clocksignal transmitter 105 may be a transmitter associated with the stimulusmaterial presentation mechanism 101, a transmitter associated with theEEG headset, a cell tower or satellite, etc. According to some examples,the stimulus material presentation mechanism 101 and data collectionmechanisms 141, 143, 145, and 147 also have clock signal receivers.

Materials eliciting neuro-responses from subjects 131, 133, 135, and 137may include people, activities, brand images, information, performances,entertainment, advertising, and may involve particular tastes, smells,sights, textures and/or sounds. In some examples, stimulus material isselected for presentation to subjects 131, 133, 135, and 137. In otherexamples, stimulus material subjects are exposed to during normaleveryday activities such as driving to work or going to the grocerystore are analyzed. Continuous and discrete modes are supported.

According to some examples, the subjects 131, 133, 135, and 137 areconnected to neuro-response data collection mechanisms 141, 143, 145,and 147. The data collection mechanisms 105 includes EEG electrodes,although in some implementations may also include a variety ofneuro-response measurement mechanisms including neurological andneurophysiological measurements systems such as EOG, GSR, EKG, pupillarydilation, eye tracking, facial emotion encoding, and reaction timedevices, etc. According to some examples, neuro-response data includescentral nervous system, autonomic nervous system, and/or effector data.

The neuro-response data collection mechanisms 141, 143, 145, and 147collect neuro-response data from multiple sources. According to someexamples, data collection mechanisms include central nervous systemsources (EEG), autonomic nervous system sources (EKG, pupillarydilation), and effector sources (EOG, eye tracking, facial emotionencoding, reaction time). In some examples, data collected is digitallysampled and stored for later analysis. In some examples, the datacollected can be analyzed in real-time. According to some examples, thedigital sampling rates are adaptively chosen based on theneurophysiological and neurological data being measured.

In an example, the neuro-response data collection mechanism includes EEGmeasurements made using scalp level electrodes, EOG measurements madeusing shielded electrodes to track eye data, and a facial affect graphicand video analyzer adaptively derived for each individual.

In some examples, the data collection mechanisms 141, 143, 145, and 147also include a condition evaluation subsystem that provides autotriggers, alerts and status monitoring and visualization components thatcontinuously monitor the status of the subject, the direction ofattention, stimulus being presented, data being collected, and the datacollection instruments. For example, the data collection mechanisms mayrecord neuro-response data while a recorder determines that a subject islistening to a particular song.

The condition evaluation subsystem may also present visual alerts andautomatically trigger remedial actions. According to some examples, thedata collection devices include mechanisms for not only monitoringsubject neuro-response to stimulus materials, but also includemechanisms for identifying and monitoring the stimulus materials. Forexample, data collection mechanisms 105 may be synchronized with aset-top box to monitor channel changes. In other examples, datacollection mechanisms 105 may be directionally synchronized to monitorwhen a subject is no longer paying attention to stimulus material. Instill other examples, the data collection mechanisms 105 may receive andstore stimulus material generally being viewed by the subject, whetherthe stimulus is a program, a commercial, printed material, or a sceneoutside a window of a living room. The data collected allows analysis ofneuro-response information and correlation of the information to actualstimulus material and not mere subject distractions.

According to some examples, the neuro-response collection system alsoincludes a data cleanser. In some examples, the data cleanser devicefilters the collected data to remove noise, artifacts, and otherirrelevant data using fixed and adaptive filtering, weighted averaging,advanced component extraction (like PCA, ICA), vector and componentseparation methods, etc. This device cleanses the data by removing bothexogenous noise (where the source is outside the physiology of thesubject, e.g. a phone ringing while a subject is viewing a video) andendogenous artifacts (where the source could be neurophysiological, e.g.muscle movements, eye blinks, etc.).

The artifact removal subsystem includes mechanisms to selectivelyisolate and review the response data and identify epochs with timedomain and/or frequency domain attributes that correspond to artifactssuch as line frequency, eye blinks, and muscle movements. The artifactremoval subsystem then cleanses the artifacts by either omitting theseepochs, or by replacing these epoch data with an estimate based on theother clean data (for example, an EEG nearest neighbor weightedaveraging approach).

According to some examples, the data cleanser device is implementedusing hardware, firmware, and/or software and may be integrated into EEGheadsets, computer systems, or data analyzers. It should be noted thatalthough a data cleanser device may have a location and functionalitythat varies based on system implementation.

The data cleanser can pass data to the data analyzer 181. The dataanalyzer 181 uses a variety of mechanisms to analyze underlying data inthe system to determine neuro-response characteristics associated withcorresponding stimulus material. According to some examples, the dataanalyzer customizes and extracts the independent neurological andneuro-physiological parameters for each individual in each modality, andblends the estimates within a modality as well as across modalities toelicit an enhanced response to the stimulus material. In some examples,stimulus material recorded using images, video, or audio is synchronizedwith neuro-response data. In some examples, the data analyzer 181aggregates the response measures across subjects in a dataset.

According to some examples, neurological and neuro-physiologicalsignatures are measured using time domain analyses and frequency domainanalyses. Such analyses use parameters that are common acrossindividuals as well as parameters that are unique to each individual.The analyses could also include statistical parameter extraction andfuzzy logic based attribute estimation from both the time and frequencycomponents of the synthesized response.

In some examples, statistical parameters used in a blended effectivenessestimate include evaluations of skew, peaks, first and second moments,population distribution, as well as fuzzy estimates of attention,emotional engagement and memory retention responses.

According to some examples, the data analyzer 181 may include anintra-modality response synthesizer and a cross-modality responsesynthesizer. In some examples, the intra-modality response synthesizeris configured to customize and extract the independent neurological andneurophysiological parameters for each individual in each modality andblend the estimates within a modality analytically to elicit an enhancedresponse to the presented stimuli. In some examples, the intra-modalityresponse synthesizer also aggregates data from different subjects in adataset.

According to some examples, the cross-modality response synthesizer orfusion device blends different intra-modality responses, including rawsignals and signals output. The combination of signals enhances themeasures of effectiveness within a modality. The cross-modality responsefusion device can also aggregate data from different subjects in adataset.

According to some examples, the data analyzer 181 also includes acomposite enhanced effectiveness estimator (CEEE) that combines theenhanced responses and estimates from each modality to provide a blendedestimate of the effectiveness. In some examples, blended estimates areprovided for each exposure of a subject to stimulus materials. Accordingto some examples, numerical values are assigned to each blendedestimate. The numerical values may correspond to the intensity ofneuro-response measurements, the significance of peaks, the changebetween peaks, etc. Higher numerical values may correspond to highersignificance in neuro-response intensity. Lower numerical values maycorrespond to lower significance or even insignificant neuro-responseactivity. In other examples, multiple values are assigned to eachblended estimate. In still other examples, blended estimates ofneuro-response significance are graphically represented to show changesafter repeated exposure.

According to some examples, the data analyzer 181 provides analyzed andenhanced response data to a response integration system 185. Accordingto some examples, the response integration system 185 combines analyzedand enhanced responses to the stimulus material while using informationabout stimulus material attributes. In some examples, the responseintegration system 185 also collects and integrates user behavioral andsurvey responses with the analyzed and enhanced response data to moreeffectively measure and neuro-response data collected in a distributedenvironment.

According to some examples, the response integration system 185 obtainscharacteristics of stimulus material such as requirements and purposesof the stimulus material. Some of these requirements and purposes may beobtained from a stimulus attribute repository. Others may be obtainedfrom other sources. Characteristics may include views and presentationspecific attributes such as audio, video, imagery and messages needed,media for enhancement, media for avoidance, etc.

According to some examples, the response integration system 185 alsoincludes mechanisms for the collection and storage of demographic,statistical and/or survey based responses to different entertainment,marketing, advertising and other audio/visual/tactile/olfactorymaterial. If this information is stored externally, the responseintegration system 185 can include a mechanism for the push and/or pullintegration of the data, such as querying, extraction, recording,modification, and/or updating.

According to some examples, the response integration system 185integrates the requirements for the presented material, the assessedneuro-physiological and neuro-behavioral response measures, and theadditional stimulus attributes such as demographic/statistical/surveybased responses into a synthesized measure for various stimulus materialconsumed by users in various environments.

According to some examples, the response integration system 185 providesstimulus and response repository 187 with data including integratedand/or individual stimulus material responses, stimulus attributes,synthesized measures, stimulus material, etc. A variety of data can bestored for later analysis, management, manipulation, and retrieval. Insome examples, the repository 187 could be used for tracking stimulusattributes and presentation attributes, audience responses andoptionally could also be used to integrate audience measurementinformation.

According to some examples, the information stored in the repositorysystem 187 could be used to assess the audience response toprograms/advertisements in multiple regions, across multipledemographics and multiple time spans (days, weeks, months, years, etc.),determine the effectiveness of billboards, monitor neuro-responses tovideo games and entertainment, etc.

As with a variety of the components in the neuro-response collectionsystem, the response integration system can be co-located with the restof the system and the user, or could be implemented in a remotelocation. It could also be optionally separated into an assessmentrepository system that could be centralized or distributed at theprovider or providers of the stimulus material. In other examples, theresponse integration system is housed at the facilities of a third partyservice provider accessible by stimulus material providers and/or users.

FIGS. 2A-2E illustrate a particular example of a neuro-response datacollection mechanism. FIG. 2A shows a perspective view of aneuro-response data collection mechanism including multiple dryelectrodes. According to some examples, the neuro-response datacollection mechanism is a headset having point or teeth electrodesconfigured to contact the scalp through hair without the use ofelectro-conductive gels. In some examples, each electrode isindividually amplified and isolated to enhance shielding androutability. In some examples, each electrode has an associatedamplifier implemented using a flexible printed circuit. Signals may berouted to a controller/processor for immediate transmission to a dataanalyzer or stored for later analysis. A controller/processor may beused to synchronize neuro-response data with stimulus materials. Theneuro-response data collection mechanism may also have receivers forreceiving clock signals and processing neuro-response signals. Theneuro-response data collection mechanisms may also have transmitters fortransmitting clock signals and sending data to a remote entity such as adata analyzer.

FIGS. 2B-2E illustrate top, side, rear, and perspective views of theneuro-response data collection mechanism. The neuro-response datacollection mechanism includes multiple electrodes including right sideelectrodes 261 and 263, left side electrodes 221 and 223, frontelectrodes 231 and 233, and rear electrode 251. It should be noted thatspecific electrode arrangement may vary from implementation toimplementation. However, example techniques and mechanisms of thepresent disclosure avoid placing electrodes on the temporal region toprevent collection of signals generated based on muscle contractions.Avoiding contact with the temporal region also enhances comfort duringsustained wear.

According to some examples, forces applied by electrodes 221 and 223counterbalance forces applied by electrodes 261 and 263. In someexamples, forces applied by electrodes 231 and 233 counterbalance forcesapplied by electrode 251. In some examples, the EEG dry electrodesoperate to detect neurological activity with minimal interference fromhair and without use of any electrically conductive gels. According tosome examples, neuro-response data collection mechanism also includesEOG sensors such as sensors used to detect eye movements.

According to some examples, data acquisition using electrodes 221, 223,231, 233, 251, 261, and 263 is synchronized with stimulus materialpresented to a user. Data acquisition can be synchronized with stimulusmaterial presented by using a shared clock signal. The shared clocksignal may originate from the stimulus material presentation mechanism,a headset, a cell tower, a satellite, etc. The data collection mechanism201 also includes a transmitter and/or receiver to send collectedneuro-response data to a data analysis system and to receive clocksignals as needed. In some examples, a transceiver transmits allcollected media such as video and/or audio, neuro-response, and sensordata to a data analyzer. In other examples, a transceiver transmits onlyinteresting data provided by a filter. According to some examples,neuro-response data is correlated with timing information for stimulusmaterial presented to a user.

In some examples, the transceiver can be connected to a computer systemthat then transmits data over a wide area network to a data analyzer. Inother examples, the transceiver sends data over a wide area network to adata analyzer. Other components such as fMRI and MEG that are not yetportable but may become portable at some point may also be integratedinto a headset.

It should be noted that some components of a neuro-response datacollection mechanism have not been shown for clarity. For example, abattery may be required to power components such as amplifiers andtransceivers. Similarly, a transceiver may include an antenna that issimilarly not shown for clarity purposes. It should also be noted thatsome components are also optional. For example, filters or storage maynot be required.

FIG. 3 illustrates examples of data models that can be used for storageof information associated with collection of neuro-response data.According to some examples, a dataset data model 301 includes a name 303and/or identifier, client attributes 305, a subject pool 307, logisticsinformation 309 such as the location, date, and stimulus material 311identified using user entered information or video and audio detection.

In some examples, a subject attribute data model 315 includes a subjectname 317 and/or identifier, contact information 321, and demographicattributes 319 that may be useful for review of neurological andneuro-physiological data. Some examples of pertinent demographicattributes include marriage status, employment status, occupation,household income, household size and composition, ethnicity, geographiclocation, sex, race. Other fields that may be included in data model 315include shopping preferences, entertainment preferences, and financialpreferences. Shopping preferences include favorite stores, shoppingfrequency, categories shopped, favorite brands. Entertainmentpreferences include network/cable/satellite access capabilities,favorite shows, favorite genres, and favorite actors. Financialpreferences include favorite insurance companies, preferred investmentpractices, banking preferences, and favorite online financialinstruments. A variety of subject attributes may be included in asubject attributes data model 315 and data models may be preset orcustom generated to suit particular purposes.

Other data models may include a data collection data model 337.According to some examples, the data collection data model 337 includesrecording attributes 339, equipment identifiers 341, modalities recorded343, and data storage attributes 345. In some examples, equipmentattributes 341 include an amplifier identifier and a sensor identifier.

Modalities recorded 343 may include modality specific attributes likeEEG cap layout, active channels, sampling frequency, and filters used.EOG specific attributes include the number and type of sensors used,location of sensors applied, etc. Eye tracking specific attributesinclude the type of tracker used, data recording frequency, data beingrecorded, recording format, etc. According to some examples, datastorage attributes 345 include file storage conventions (format, namingconvention, dating convention), storage location, archival attributes,expiry attributes, etc.

A preset query data model 349 includes a query name 351 and/oridentifier, an accessed data collection 353 such as data segmentsinvolved (models, databases/cubes, tables, etc.), access securityattributes 355 included who has what type of access, and refreshattributes 357 such as the expiry of the query, refresh frequency, etc.Other fields such as push-pull preferences can also be included toidentify an auto push reporting driver or a user driven report retrievalsystem.

FIG. 4 illustrates examples of queries that can be performed to obtaindata associated with neuro-response data collection. According to someexamples, queries are defined from general or customized scriptinglanguages and constructs, visual mechanisms, a library of presetqueries, diagnostic querying including drill-down diagnostics, andeliciting what if scenarios. According to some examples, subjectattributes queries 415 may be configured to obtain data from aneuro-informatics repository using a location 417 or geographicinformation, session information 421 such as timing information for thedata collected. Location information 423 may also be collected. In someexamples, a neuro-response data collection mechanism includes GPS orother location detection mechanisms. Demographics attributes 419 includehousehold income, household size and status, education level, age ofkids, etc.

Other queries may retrieve stimulus material recorded based on shoppingpreferences of subject participants, countenance, physiologicalassessment, completion status. For example, a user may query for dataassociated with product categories, products shopped, shops frequented,subject eye correction status, color blindness, subject state, signalstrength of measured responses, alpha frequency band ringers, musclemovement assessments, segments completed, etc.

Response assessment based queries 437 may include attention scores 439,emotion scores, 441, retention scores 443, and effectiveness scores 445.Such queries may obtain materials that elicited particular scores.Response measure profile based queries may use mean measure thresholds,variance measures, number of peaks detected, etc. Group response queriesmay include group statistics like mean, variance, kurtosis, p-value,etc., group size, and outlier assessment measures. Still other queriesmay involve testing attributes like test location, time period, testrepetition count, test station, and test operator fields. A variety oftypes and combinations of types of queries can be used to efficientlyextract data.

FIG. 5 illustrates examples of reports that can be generated. Accordingto some examples, client assessment summary reports 501 includeeffectiveness measures 503, component assessment measures 505, andneuro-response data collection measures 507. Effectiveness assessmentmeasures include composite assessment measure(s),industry/category/client specific placement (percentile, ranking, etc.),actionable grouping assessment such as removing material, modifyingsegments, or fine tuning specific elements, etc, and the evolution ofthe effectiveness profile over time. In some examples, componentassessment reports include component assessment measures like attention,emotional engagement scores, percentile placement, ranking, etc.Component profile measures include time based evolution of the componentmeasures and profile statistical assessments. According to someexamples, reports include the number of times material is assessed,attributes of the multiple presentations used, evolution of the responseassessment measures over the multiple presentations, and usagerecommendations.

According to some examples, client cumulative reports 511 include mediagrouped reporting 513 of all stimulus assessed, campaign groupedreporting 515 of stimulus assessed, and time/location grouped reporting517 of stimulus assessed. According to some examples, industrycumulative and syndicated reports 521 include aggregate assessmentresponses measures 523, top performer lists 525, bottom performer lists527, outliers 529, and trend reporting 531. In some examples, trackingand reporting includes specific products, categories, companies, brands.

FIG. 6 illustrates one example of neuro-response data collection. At601, user information is received from a subject provided with aneuro-response data collection mechanism. According to some examples,the subject sends data including age, gender, income, location,interest, ethnicity, etc. after being provided with an EEG headsetincluding EEG electrodes.

At 603, neuro-response data is received from the subject neuro-responsedata collection mechanism. In some examples, EEG, EOG, pupillarydilation, facial emotion encoding data, video, images, audio, GPS data,etc., can all be transmitted from the subject to a neuro-response dataanalyzer. In some examples, only EEG data is transmitted. According tosome examples, neuro-response and associated data is transmitteddirectly from an EEG cap wide area network interface to a data analyzer.In some examples, neuro-response and associated data is transmitted to acomputer system that then performs compression and filtering of the databefore transmitting the data to a data analyzer over a network.

According to some examples, data is also passed through a data cleanserto remove noise and artifacts that may make data more difficult tointerpret. According to some examples, the data cleanser removes EEGelectrical activity associated with blinking and otherendogenous/exogenous artifacts. Data cleansing may be performed beforeor after data transmission to a data analyzer.

At 605, stimulus material is identified. According to some examples,stimulus material is identified based on user input or system data. Eyetracking movements can determine where user attention is focused at anygiven time. At 607, neuro-response data is synchronized with timing,location, and other stimulus material data. In some examples,neuro-response data is synchronized with a shared clock source.According to some examples, neuro-response data such as EEG and EOG datais tagged to indicate what the subject is viewing or listening to at aparticular time.

At 609, data analysis is performed. Data analysis may includeintra-modality response synthesis and cross-modality response synthesisto enhance effectiveness measures. It should be noted that in someparticular instances, one type of synthesis may be performed withoutperforming other types of synthesis. For example, cross-modalityresponse synthesis may be performed with or without intra-modalitysynthesis.

A variety of mechanisms can be used to perform data analysis 609. Insome examples, a stimulus attributes repository is accessed to obtainattributes and characteristics of the stimulus materials, along withpurposes, intents, objectives, etc. In some examples, EEG response datais synthesized to provide an enhanced assessment of effectiveness.According to some examples, EEG measures electrical activity resultingfrom thousands of simultaneous neural processes associated withdifferent portions of the brain. EEG data can be classified in variousbands. According to some examples, brainwave frequencies include delta,theta, alpha, beta, and gamma frequency ranges. Delta waves areclassified as those less than 4 Hz and are prominent during deep sleep.Theta waves have frequencies between 3.5 to 7.5 Hz and are associatedwith memories, attention, emotions, and sensations. Theta waves aretypically prominent during states of internal focus.

Alpha frequencies reside between 7.5 and 13 Hz and typically peak around10 Hz. Alpha waves are prominent during states of relaxation. Beta waveshave a frequency range between 14 and 30 Hz. Beta waves are prominentduring states of motor control, long range synchronization between brainareas, analytical problem solving, judgment, and decision making. Gammawaves occur between 30 and 60 Hz and are involved in binding ofdifferent populations of neurons together into a network for the purposeof carrying out a certain cognitive or motor function, as well as inattention and memory. Because the skull and dermal layers attenuatewaves in this frequency range, brain waves above 75-80 Hz are difficultto detect and are often not used for stimuli response assessment.

However, example techniques and mechanisms of the present disclosurerecognize that analyzing high gamma band (kappa-band: Above 60 Hz)measurements, in addition to theta, alpha, beta, and low gamma bandmeasurements, enhances neurological attention, emotional engagement andretention component estimates. In some examples, EEG measurementsincluding difficult to detect high gamma or kappa band measurements areobtained, enhanced, and evaluated. Subject and task specific signaturesub-bands in the theta, alpha, beta, gamma and kappa bands areidentified to provide enhanced response estimates. According to someexamples, high gamma waves (kappa-band) above 80 Hz (typicallydetectable with sub-cranial EEG and/or magnetoencephalography) can beused in inverse model-based enhancement of the frequency responses tothe stimuli.

Some examples disclosed herein recognize that particular sub-bandswithin each frequency range have particular prominence during certainactivities. A subset of the frequencies in a particular band is referredto herein as a sub-band. For example, a sub-band may include the 40-45Hz range within the gamma band. In some examples, multiple sub-bandswithin the different bands are selected while remaining frequencies areband pass filtered. In some examples, multiple sub-band responses may beenhanced, while the remaining frequency responses may be attenuated.

An information theory based band-weighting model is used for adaptiveextraction of selective dataset specific, subject specific, taskspecific bands to enhance the effectiveness measure. Adaptive extractionmay be performed using fuzzy scaling. Stimuli can be presented andenhanced measurements determined multiple times to determine thevariation profiles across multiple presentations. Determining variousprofiles provides an enhanced assessment of the primary responses aswell as the longevity (wear-out) of the marketing and entertainmentstimuli. The synchronous response of multiple individuals to stimulipresented in concert is measured to determine an enhanced across subjectsynchrony measure of effectiveness. According to some examples, thesynchronous response may be determined for multiple subjects residing inseparate locations or for multiple subjects residing in the samelocation.

Although a variety of synthesis mechanisms are described, it should berecognized that any number of mechanisms can be applied in sequence orin parallel with or without interaction between the mechanisms.

Although intra-modality synthesis mechanisms provide enhancedsignificance data, additional cross-modality synthesis mechanisms canalso be applied. A variety of mechanisms such as EEG, Eye Tracking, GSR,EOG, and facial emotion encoding are connected to a cross-modalitysynthesis mechanism. Other mechanisms as well as variations andenhancements on existing mechanisms may also be included. According tosome examples, data from a specific modality can be enhanced using datafrom one or more other modalities. In some examples, EEG typically makesfrequency measurements in different bands like alpha, beta and gamma toprovide estimates of significance. However, example techniques of thepresent disclosure recognize that significance measures can be enhancedfurther using information from other modalities.

For example, facial emotion encoding measures can be used to enhance thevalence of the EEG emotional engagement measure. EOG and eye trackingsaccadic measures of object entities can be used to enhance the EEGestimates of significance including but not limited to attention,emotional engagement, and memory retention. According to some examples,a cross-modality synthesis mechanism performs time and phase shifting ofdata to allow data from different modalities to align. In some examples,it is recognized that an EEG response will often occur hundreds ofmilliseconds before a facial emotion measurement changes. Correlationscan be drawn and time and phase shifts made on an individual as well asa group basis. In other examples, saccadic eye movements may bedetermined as occurring before and after particular EEG responses.According to some examples, time corrected GSR measures are used toscale and enhance the EEG estimates of significance including attention,emotional engagement and memory retention measures.

Evidence of the occurrence or non-occurrence of specific time domaindifference event-related potential components (like the DERP) inspecific regions correlates with subject responsiveness to specificstimulus. According to some examples, ERP measures are enhanced usingEEG time-frequency measures (ERPSP) in response to the presentation ofthe marketing and entertainment stimuli. Specific portions are extractedand isolated to identify ERP, DERP and ERPSP analyses to perform. Insome examples, an EEG frequency estimation of attention, emotion andmemory retention (ERPSP) is used as a co-factor in enhancing the ERP,DERP and time-domain response analysis.

EOG measures saccades to determine the presence of attention to specificobjects of stimulus. Eye tracking measures the subject's gaze path,location and dwell on specific objects of stimulus. According to someexamples, EOG and eye tracking is enhanced by measuring the presence oflambda waves (a neurophysiological index of saccade effectiveness) inthe ongoing EEG in the occipital and extra striate regions, triggered bythe slope of saccade-onset to estimate the significance of the EOG andeye tracking measures. In some examples, specific EEG signatures ofactivity such as slow potential shifts and measures of coherence intime-frequency responses at the Frontal Eye Field (FEF) regions thatpreceded saccade-onset are measured to enhance the effectiveness of thesaccadic activity data.

According to some examples, facial emotion encoding uses templatesgenerated by measuring facial muscle positions and movements ofindividuals expressing various emotions prior to the testing session.These individual specific facial emotion encoding templates are matchedwith the individual responses to identify subject emotional response. Insome examples, these facial emotion encoding measurements are enhancedby evaluating inter-hemispherical asymmetries in EEG responses inspecific frequency bands and measuring frequency band interactions.Example techniques of the present disclosure recognize that not only areparticular frequency bands significant in EEG responses, but particularfrequency bands used for communication between particular areas of thebrain are significant. Consequently, these EEG responses enhance theEMG, graphic and video based facial emotion identification.

Integrated responses are generated at 611. According to some examples,the data communication device transmits data to the response integrationusing protocols such as the File Transfer Protocol (FTP), HypertextTransfer Protocol (HTTP) along with a variety of conventional, bus,wired network, wireless network, satellite, and proprietarycommunication protocols. The data transmitted can include the data inits entirety, excerpts of data, converted data, and/or elicited responsemeasures. According to some examples, data is sent using atelecommunications, wireless, Internet, satellite, or any othercommunication mechanisms that is capable of conveying information frommultiple subject locations for data integration and analysis. Themechanism may be integrated in a set top box, computer system, receiver,mobile device, etc.

In some examples, the data communication device sends data to theresponse integration system. According to some examples, the responseintegration system combines analyzed and enhanced responses to thestimulus material while using information about stimulus materialattributes. In some examples, the response integration system alsocollects and integrates user behavioral and survey responses with theanalyzed and enhanced response data to more effectively measure andtrack neuro-responses to stimulus materials. According to some examples,the response integration system obtains attributes such as requirementsand purposes of the stimulus material presented.

Some of these requirements and purposes may be obtained from a varietyof databases. According to some examples, the response integrationsystem also includes mechanisms for the collection and storage ofdemographic, statistical and/or survey based responses to differententertainment, marketing, advertising and otheraudio/visual/tactile/olfactory material. If this information is storedexternally, the response integration system can include a mechanism forthe push and/or pull integration of the data, such as querying,extraction, recording, modification, and/or updating.

The response integration system can further include an adaptive learningcomponent that refines user or group profiles and tracks variations inthe neuro-response data collection system to particular stimuli orseries of stimuli over time. This information can be made available forother purposes, such as use of the information for presentationattribute decision making. According to some examples, the responseintegration system builds and uses responses of users having similarprofiles and demographics to provide integrated responses at 611. Insome examples, stimulus and response data is stored in a repository at613 for later retrieval and analysis.

According to some examples, at least some of the example mechanisms suchas the data collection mechanisms, the intra-modality synthesismechanisms, cross-modality synthesis mechanisms, etc. are implemented onmultiple devices. However, it is also possible that the examplemechanisms be implemented in hardware, firmware, and/or software in asingle system. FIG. 7 provides one example of a system 700 that can beused to implement one or more mechanisms. For example, the system 700shown in FIG. 7 may be used to implement a data analyzer.

The example system 700, which is suitable for implementing some examplesdisclosed herein, includes a processor 701, a memory 703, an interface711, and a bus 715 (e.g., a PCI bus). When acting under the control ofappropriate software or firmware, the processor 701 is responsible forsuch tasks such as pattern generation. Various specially configureddevices can also be used in place of a processor 701 or in addition toprocessor 701. The complete implementation can also be done in customhardware. The interface 711 is typically configured to send and receivedata packets or data segments over a network. Particular examples ofinterfaces the device supports include host bus adapter (HBA)interfaces, Ethernet interfaces, frame relay interfaces, cableinterfaces, DSL interfaces, token ring interfaces, and the like.

In addition, various very high-speed interfaces may be provided such asfast Ethernet interfaces, Gigabit Ethernet interfaces, ATM interfaces,HSSI interfaces, POS interfaces, FDDI interfaces and the like.Generally, these interfaces may include ports appropriate forcommunication with the appropriate media. In some cases, they may alsoinclude an independent processor and, in some instances, volatile RAM.The independent processors may control such communications intensivetasks as data synthesis.

According to some examples, the system 700 uses memory 703 to storedata, algorithms and program instructions. The program instructions maycontrol the operation of an operating system and/or one or moreapplications, for example. The memory or memories may also be configuredto store received data and process received data.

Because such information and program instructions may be employed toimplement the systems/methods described herein, some examples employtangible, machine readable media that include program instructions,state information, etc. for performing various operations describedherein. Examples of machine-readable media include, but are not limitedto, magnetic media such as hard disks, floppy disks, and magnetic tape;optical media such as CD-ROM disks and DVDs; magneto-optical media suchas optical disks; and hardware devices that are specially configured tostore and perform program instructions, such as read-only memory devices(ROM) and random access memory (RAM). Examples of program instructionsinclude both machine code, such as produced by a compiler, and filescontaining higher level code that may be executed by the computer usingan interpreter.

Although the foregoing examples have been described in some detail forpurposes of clarity of understanding, it will be apparent that certainchanges and modifications may be practiced within the scope of theclaims. Therefore, the present examples are to be considered asillustrative and not restrictive and the present disclosure is not to belimited to the details given herein, but may be modified within thescope and equivalents of the claims.

What is claimed is:
 1. A system comprising: a headset to gather dataincluding first neuro-response data and second neuro-response data froma user while the user is exposed to stimulus material, the headsetincluding: a band; a first sensor extending from the band to be locatedon a first position on a head of the user when the user is wearing theheadset, the first sensor to gather the first neuro-response data, thefirst neuro-response data including at least one ofelectroencephalographic data or magnetoencephalographic data; a secondsensor extending from the band to be located on a second position on thehead of the user when the user is wearing the headset, the second sensorto gather second neuro-response data, the second neuro-response dataincluding at least one of electroencephalographic data ormagnetoencephalographic data, wherein when coupled to the band, thefirst sensor and the second sensor are rotatably positionable relativeto the band, wherein rotation of the first sensor from the firstposition to a third position on the head of the user rotates the secondsensor relative to the band from the second position to a fourthposition on the head of the user when the user is wearing the headset;and a processor programmed to: synchronize the first neuro-responsedata, the second neuro-response data and the stimulus material togenerate synchronized data; and determine an effectiveness of a portionof the stimulus material based on the synchronized data.
 2. The systemof claim 1, wherein the headset further includes an identifier toidentify the stimulus material.
 3. The system of claim 1, wherein thefirst sensor and the second sensor are electrodes and the processor isprogrammed to process signals from active ones of the electrodes foranalysis.
 4. The system of claim 1, wherein the first sensor and thesecond sensor are electrodes and the processor is programmed to processsignals from a subset of the plurality of electrodes for analysis thatare active channels.
 5. The system of claim 1, wherein the headset is afirst headset, the data is first data, and the user is a first user,further including a second headset to gather second data from a seconduser while the second user is exposed to the stimulus material.
 6. Thesystem of claim 5, further including a receiver to receive the firstdata from the first user and the second data from the second user, theeffectiveness based on the first data and the second data.
 7. The systemof claim 1, further including a third sensor to gather eye-trackingdata, wherein the processor is programmed to analyze the eye-trackingdata to determine a direction of the attention of the user.
 8. Thesystem of claim 1, wherein the headset further includes a firstrotatable hub and the first sensor includes a first electrode coupled tothe first hub at a first distance.
 9. The system of claim 8, wherein thesecond sensor includes a second electrode coupled to the first hub at asecond distance, the second distance different than the first distance.10. The system of claim 9, wherein the headset further includes a secondrotatable hub and a third electrode and a fourth electrode extend fromthe second hub.
 11. The system of claim 1, further including a thirdsensor to gather facial data, the facial data including facial emotionencoding data.
 12. The system of claim 11, wherein the third sensorincludes a facial affect graphic and video analyzer.
 13. The system ofclaim 11, wherein the headset further includes a recorder to record thestimulus material.
 14. The system of claim 13, wherein the headsetfurther includes a transmitter to transmit the recorded stimulusmaterial and one or more of the first neuro-response data, the secondneuro-response data, the facial data, the synchronized data, or thedetermined effectiveness to a remote processor.
 15. The system of claim11, wherein the processor is programmed to determine when the user isnot paying attention to the stimulus material based on at least one ofthe first neuro-response data, the second neuro-response data, thefacial data, or the synchronized data.
 16. A method comprising:collecting data with a headset worn by a user while the user is exposedto stimulus material, the data including (1) first neuro-response datagathered by a first sensor carried by a band, the first neuro-responsedata including at least one of electroencephalographic data ormagnetoencephalographic data, and (2) second neuro-response datagathered by a second sensor carried by the band, the secondneuro-response data including at least one of electroencephalographicdata or magnetoencephalographic data, wherein when coupled to the band,the first sensor and the second sensor are rotatably positionablerelative to the band, wherein rotation of the first sensor from a firstposition on a head of the user to a second position on the head of theuser rotates the second sensor relative to the band from a thirdposition on the head of the user to a fourth position on the head of theuser; generating synchronized data with a processor by synchronizing thefirst neuro-response data, the second neuro-response data and thestimulus material; and determining an effectiveness of a portion of thestimulus material based on the synchronized data.
 17. The method ofclaim 16, wherein the headset is a first headset, the data is firstdata, and the user is a first user, further including collecting seconddata with a second headset worn by a second user while the second useris exposed to the stimulus material.
 18. The method of claim 17, furtherincluding synchronizing the synchronized data from the first user andthe second data from the second user to determine a synchronized measureof effectiveness.
 19. The method of claim 17, wherein the first user andthe second user are in different geographical locations when exposed tothe stimulus material.
 20. The method of claim 19, wherein the differentgeographical locations are different buildings.
 21. The method of claim16 further including analyzing eye-tracking data gathered by a thirdsensor to determine a direction of attention of the user.
 22. The methodof claim 16 further including receiving, via a clock signal receiver onthe headset, a clock signal representing timing information of thestimulus material, wherein the synchronized data is generated by theprocessor based on the timing information.
 23. The method of claim 22,wherein the clock signal receiver is a cell tower signal receiver or asatellite signal receiver, and wherein receiving the clock signalincludes receiving the clock signal from a cell tower or a satellite.24. The method of claim 22, wherein receiving the clock signal includesreceiving the clock signal from a stimulus presentation device thatpresents the stimulus material to the user.
 25. The method of claim 16,wherein the data further includes facial data gathered by a thirdsensor, the facial data including facial emotion encoding data.